P BMI IS NOT NORMALLY DISTRIBUTED X Right skew (long tail to the right)

 A

You’re right — BMI is typically not normally distributed in most real populations.

Here’s why, and what that means in practice:

Why BMI isn’t normal

• Right-skewed distribution: There’s a hard lower bound (you can’t have negative BMI), but no strict upper bound. A smaller number of very high BMIs pull the tail to the right.

• Population heterogeneity: Mixing ages, sexes, ethnicities, and health statuses creates multiple sub-patterns instead of one clean bell curve.

• Biological + social factors: Diet, activity, socioeconomic status, and disease don’t affect BMI symmetrically.

So instead of a nice bell shape, BMI usually looks skewed, sometimes even bimodal.

Why this matters

If BMI isn’t normal, then:

• The mean > median (mean gets pulled upward)

• t-tests, ANOVA, linear regression assumptions may be violated

• Confidence intervals and p-values can be misleading if you don’t adjust

What to do instead

Depending on your goal:

Descriptive stats

• Report median + IQR instead of mean ± SD

Comparisons

• Use non-parametric tests (Mann–Whitney, Kruskal–Wallis)

• Or analyze BMI categories (underweight / normal / overweight / obese)

Modeling

• Transform BMI (e.g., log(BMI))

• Use robust regression

• Or model a related measure (e.g., waist circumference) if appropriate

Large samples note

• With big datasets, parametric tests can still work due to the Central Limit Theorem, but interpretation still needs care.

If you want, tell me what you’re doing with BMI (paper, regression, hypothesis test, clinical report), and I’ll suggest the cleanest statistical approach for that use case.